101 to 125 of 402 Pandas Jobs

Background in Physics, Math, Engineering, Statistics, or Economics, with a strong focus on Modelling and Quantitative Finance. High-level programming skills in Python and experience with numerical libraries (NumPy, Pandas, SciPy). Proven experience working with time series data, market simulations, risk metrics, and calibration techniques. Strong understanding of financial products, risk factors, and trading exposure. Excellent English and Spanish More ❯

years of professional software development with exposure to capital markets or trading (equities and/or execution). Technical Skills: Proficient in Python with some exposure to data analysis (Pandas, NumPy) or similar (R, MATLAB). Solid background in object-oriented programming (Java preferred; C++ or C# also valued). Familiarity with Linux-based systems (configuration, scripting, deployment). Docker More ❯

Skills: Proven experience as a Machine Learning Engineer or similar role. Strong knowledge of machine learning algorithms, statistics, and predictive modeling Proficiency with Python and machine learning toolkits like pandas, scikit-learn, and optionally pytorch/tensorflow. Experience with machine learning operations (MLOps) and productionization of ML models. Familiarity with building data and metric generation pipelines, using tools like SQL More ❯

a technical and non-technical audience Independent and self-driven learner, able to step outside of their area of expertise Python; we work with asyncio, SQLAlchemy, FastAPI, Pydantic, NumPy, Pandas SQL; performance tuning, schema design, monitoring in production, we mainly work with PostgreSQL Cloud (AWS) deployments and monitoring, basic networking and security best practices Command line familiarity, git, automated testing More ❯

align technical implementation with requirements and mission outcomes. Requirements Minimum Qualifications: - 3-5 years of professional experience in Python development. - Strong experience with data processing libraries such as PySpark, Pandas, and NumPy. - Proficiency in API development using Python libraries such as FastAPI. - Hands-on experience with unit testing frameworks including PyTest and mocking strategies. - Demonstrated understanding of cloud-based deployment More ❯

in languages such as Python. Solid understanding of machine learning concepts, algorithms, and libraries (e.g., scikit-learn, TensorFlow, PyTorch). Experience with data manipulation and analysis using tools like Pandas and NumPy. Familiarity with cloud computing platforms (e.g., AWS, Azure, GCP). Desired Qualifications: Masters degree. Demonstrated experience with the application of machine learning and artificial intelligence within the Department More ❯

Computer Science, Engineering, Mathematics, Statistics, or Physics 3-7 years of relevant experience in a data science/ML engineering role Expert in Python and key libraries (Scikit-learn, Pandas, PyTorch, TensorFlow, XGBoost, Transformers) Strong understanding of statistics, ML algorithms, and data wrangling Familiarity with cloud platforms (Azure, AWS, GCP) and containerized workflows (Docker, Kubernetes) Knowledge of ethical and explainable More ❯

Python development experience Proven experience in AI/machine learning project implementation In-depth understanding of the Python software development stacks, ecosystems, frameworks and tools such as Numpy, Scipy, Pandas, Dask, spaCy, NLTK, sci-kit-learn and PyTorch Experience with popular Python frameworks such as Django, Flask or Pyramid Experience with Jupyter Notebooks Experience with EDA and data visualization libraries More ❯

particular asset class or market sector. Our systems are almost all running on Linux and most of our code is in Python, with the full scientific stack: numpy, scipy, pandas, scikit-learn to name a few of the open-source libraries we use extensively. Inclusion, Work-Life Balance and Benefits at Man Group You'll thrive in our working environment More ❯

the adoption of best practices in data science across the organisation, lead other data science engineers MINIMUM QUALIFICATIONS Industry experience using Python for data science (e.g. numpy, scipy, scikit, pandas, etc.) and SQL or other languages for relational databases. Experience with a cloud platform such as (AWS, GCP, Azure etc.) Experience with common data science tools; statistical analysis, mathematical modelling More ❯

Experience in both data engineering and machine learning, with a strong portfolio of relevant projects. Proficiency in Python with libraries like TensorFlow, PyTorch, or Scikit-learn for ML, and Pandas, PySpark, or similar for data processing. Experience designing and orchestrating data pipelines with tools like Apache Airflow, Spark, or Kafka. Strong understanding of SQL, NoSQL, and data modeling. Familiarity with More ❯

Experience in both data engineering and machine learning, with a strong portfolio of relevant projects. Proficiency in Python with libraries like TensorFlow, PyTorch, or Scikit-learn for ML, and Pandas, PySpark, or similar for data processing. Experience designing and orchestrating data pipelines with tools like Apache Airflow, Spark, or Kafka. Strong understanding of SQL, NoSQL, and data modeling. Familiarity with More ❯

with experience building complex, maintainable systems Professional software development experience with a track record of delivering high-quality, production-grade code Experience with scientific computing libraries such as NumPy, Pandas, or SciPy in production environments Holistic software development mindset covering testing, documentation, security, and performance Track record of mentoring other engineers and sharing knowledge across teams Working knowledge of mathematical More ❯

Recognition and Extraction, Information retrieval and query systems. LLM technologies: Qdrant, Haystack, LLamaIndex, Ollama (or also LlangChain and similar- if you like that stuff) ML and scientific libraries: Sklearn, pandas, numpy, matplot, pytorch, tensor flow. Any exposure to AI topics applied to Linux systems or open-source contributions are a plus. Other skills and characteristics you will need in this More ❯

features end-to-end, from ideation to deployment. Be on-call for urgent AI model fixes or system failures. Qualifications Proficiency in Python and related libraries (e.g., NumPy, SciPy, pandas) is required. Strong production experience with at least one framework: LangChain, AutoGen, or CrewAI. Deep understanding of agentic systems, autonomous workflows, and LLM-based automation. Experience deploying and fine-tuning More ❯

with experience building complex, maintainable systems Professional software development experience with a track record of delivering high-quality, production-grade code Experience with scientific computing libraries such as NumPy, Pandas, or SciPy in production environments Holistic software development mindset covering testing, documentation, security, and performance Track record of mentoring other engineers and sharing knowledge across teams Desirable: Working knowledge of More ❯

2+ years of experience in quantitative modeling, data science, or financial analytics, preferably in mortgage servicing, loss mitigation, or credit risk Proficiency in Python and data science packages (NumPy, Pandas, Scikit-learn, XGBoost, etc Skilled in SQL, Linux shell scripting, and working knowledge of AWS cloud infrastructure Understanding of loan performance analytics, including delinquency modeling, loan modification outcomes, and loss More ❯

have interest in Energy Markets/Financial Markets (e.g. trading products) and growth of technology. Advantageous, but not essential Any experience with Python and its data processing libraries e.g. Pandas, Py-Spark. An understanding of the basics of trade modelling and lifecycle. Basic knowledge of any energy trading regulations i.e. EMIR/REMIT/Dodd-Frank Exposure to functional programming More ❯

see from you Experience with Python A keen interest in backend development Understanding of distributed systems (RabbitMQ, Celery, Redis, Memcached) Good knowledge of scientific libraries such as Numpy and Pandas Extensive usage of MongoDB, BigQuery, data visualisation and manipulation tools Experience with designing, training, evaluating machine learning models Familiarity with the main AI frameworks (Pytorch, Tensorflow, scikit-learn) Appreciation of More ❯

see from you Experience with Python A keen interest in backend development Understanding of distributed systems (RabbitMQ, Celery, Redis, Memcached) Good knowledge of scientific libraries such as Numpy and Pandas Extensive usage of MongoDB, BigQuery, data visualisation and manipulation tools Experience with designing, training, evaluating machine learning models Familiarity with the main AI frameworks (Pytorch, Tensorflow, scikit-learn) Appreciation of More ❯

or data engineering. Ability to work standard European time-zone hours and legal authorisation to work in your country of residence. Strong experience with Python’s data ecosystem (e.g., Pandas, NumPy) and deep expertise in SQL for building robust data extraction, transformation, and analysis pipelines. Hands-on experience with big data processing frameworks such as Apache Spark, Databricks, or Snowflake More ❯

or data engineering. Ability to work standard European time-zone hours and legal authorisation to work in your country of residence. Strong experience with Python’s data ecosystem (e.g., Pandas, NumPy) and deep expertise in SQL for building robust data extraction, transformation, and analysis pipelines. Hands-on experience with big data processing frameworks such as Apache Spark, Databricks, or Snowflake More ❯

data, including knowledge of forecasting, A/B testing, entity extraction, and feature engineering. Proficiency in programming languages such as Python, R, and SQL, and data analysis libraries (e.g., Pandas, NumPy, SciPy, Tidyverse). Strong knowledge of machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, scikit-learn, NLTK). Experience with NLP techniques, such as named entity recognition (NER), topic More ❯

/or other relevant languages (e.g., R, Java, C++). Machine Learning & AI Frameworks: Experience with TensorFlow, PyTorch, Scikit-learn, or similar libraries. Data Manipulation & Analysis: Strong skills in Pandas, NumPy, and SQL for handling and analysing large datasets. Cloud Computing: Familiarity with cloud platforms such as AWS, Azure, or Google Cloud for AI model deployment. Data Visualisation: Ability to More ❯

model performance evaluation, hyperparameter tuning, and maintenance using tools like Vertex AI Pipelines. Cloud Computing (Google Cloud Platform - GCP Preferred) Technical Expertise & Tools Python: Advanced proficiency in data analysis (Pandas, NumPy), machine learning, PI development (Flask/FastAPI), and writing clean, maintainable code. SQL: Expertise in querying, database design/optimization, stored procedures, functions, partitioning/clustering strategies for BigQuery More ❯